Cardiovascular Disease Classification using ECG Signal

Cardiovascular Disease Classification using ECG Signal
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-11
Year of Publication : 2021
Authors : Salam Devayani Devi, Dr. Loitongbam Surajkumar Singh
DOI :  10.14445/22315381/IJETT-V69I11P217

How to Cite?

Salam Devayani Devi, Dr. Loitongbam Surajkumar Singh, "Cardiovascular Disease Classification using ECG Signal," International Journal of Engineering Trends and Technology, vol. 69, no. 11, pp. 134-139, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I11P217

Abstract
This paper presents an ECG signal monitoring system to classify normal and abnormal ECG signals. The system consists of pre-processing step followed by classification. In the pre-processing step, reduction of the noise contained in the ECG signal is performed. Then the filtered signal is given as the input to the classification block. A long short-term neural network is used for the classification of arrhythmia from the normal ECG signal beats. The benefit of this work is the reduction in the size and power consumption compared to other approaches. This monitoring system is implemented on Spartan 7 FPGA using Xilinx vivado software, achieving an accuracy of 99.92% with a power consumption of 0.009W. The ECG dataset used in this work is the MIT-BIH arrhythmia database.

Keywords
ECG, FPGA, LSTM, cardiovascular disease classification, abnormal ECG signal.

Reference
[1] Satria Mandala, Tham Cai Di, ECG Parameters for Malignant Ventricular Arrhythmias: A Comprehensive Review, J. Med. Biol. Eng., ( 2016).
[2] R.Shankari, S. Asha Safana, M. Kaviya, LyndaCharles, FPGA-based electrocardiography (ECG) signal analysis system using infinite impulse response (IIR) filter, in International Journal of Applied Engineering Research, 14 (2019).
[3] Dipeeka Kalamkar, S.S. P., Ecg Signal Noise Reduction Using FPGA, in International Journal Of Current Engineering And Scientific Research, 4(6) (2017).
[4] Md. Merajul Islam, Md. A. R., ECG Signal Based Angina Pectoris Detection Using Statistical and Machine Learning Approach, in International Journal of Scientific & Engineering Research, 10( 1) ( 2019).
[5] Wissam Jenka, R.L., Abdelhafid Elouardi, S. M., FPGA Implementation of the Real-Time ADTF process using the Intel-Altera DE1 Board for ECG signal Denoising, in 4th World Conference on Complex Systems (WCCS), (2019).
[6] N. S. Madiraju, N. Kurella, R. Valapudasu, FPGA Implementation of ECG feature extraction using Time domain analysis, in Electrical Engineering and Systems Science, (2018).
[7] Safa Mejhoudi, R. L., Wissam jenkal, Real-time ECG Signal Denoising Using the ADTF Algorithm for Embedded Implementation on FPGAs, 4th World Conference on Complex Systems (WCCS), (2019).
[8] M. Yin, R. T., Miao Liu, K. Han, X. Lv, M. Huang, P. Xu, Y. Hu, B. Ma, and Y. Gai, Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System, on Internet of Medical Things for Healthcare Engineering, (2021).
[9] El Hassan El Mimouni, Mohammed Karim, Novel Real-Time FPGA-Based QRS Detector Using Adaptive Threshold with the previous smallest peak of ECG signal, in Journal of Theoretical and Applied Information Technology, Vol. 50(1) (2013).
[10] Savita Jadhav, B. P., Chaudhari Asawari, C. N., FPGA Based ECG Signal Noise Suppression using Windowing Techniques, in International Journal of Engineering Trends and Technology (IJETT) , 4 (2017).
[11] Wai-Chi Fan, I-Wei Chen, An Integrated PPG and ECG Signal Processing Hardware Architecture Design of EEMD Processor, in International Conference on Consumer Electronics (ICCE), (2019).
[12] Sachin Singh, Netaji Gandhi. N, Pattern analysis of different ECG signal using Pan-Tompkin’s algorithm, in International Journal on Computer Science and Engineering 02(7) (2010) 2502-2505.
[13] D. Alhelal and M. Faezipour, Denoising and Beat Detection of ECG Signal by Using FPGA, in International Journal of High-Speed Electronics and Systems, 26 (2017).
[14] Jiapu Pan, W.J.T., A Real-Time QRS Detection Algorithm, in IEEE transactions on Biomedical Engineering, 32(3) (1985).
[15] S. M. Rayavarapu, D Bikshapathi, Samrat L Sabat, J.S.Armand Eyebe Fouda, FPGA implementation of Ordinal Pattern Analysis algorithm for Early Detection of Cardiovascular diseases, in International Conference for Convergence in Technology (I2CT), Pune, India, 29-31 (2019).
[16] K. Meddah, M. Kedir Talha, M. Bahoura, H. Zairi, FPGA-based system for heart rate monitoring, in IET Circuits, Devices & Systems, 13 (6) (2019) 771-782.
[17] Safa Mejhoudi, R. L., Wissam jenkal, Real-time ECG Signal Denoising Using the ADTF Algorithm for Embedded Implementation on FPGAs, 4th World Conference on Complex Systems (WCCS), (2019).
[18] Agostino Giorgio,C. g.: ECG Signal Denoising using Wavelet for the VLP effective detection on FPGA, AEIT International Annual Conference, (2018).
[19] Padmavathi C, Veenadevi S V, An Optimized FPGA Based System Design for the Arrhythmia Detection using ECG, in International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, 9 (2019).
[20] Syed Muhammad Anwar, Maheen Gul, Muhammad Majid, and Majdi Alnowami, Arrhythmia Classification of ECG Signals Using Hybrid Features, in Comput Math Methods Med. (2018).
[21] L.V.Rajani Kumari, Y.Padma Sai, N.Balaji, K.Viswada, FPGA Based Arrhythmia Detection, in 3rd International Conference on Recent Trends in Computing, Procedia Computer Science 57 (2015) 970 – 979 .
[22] K. Meddah, M. Kedir Talha, M. Bahoura, H. Zairi, FPGA-based system for heart rate monitoring, in IET Circuits, Devices & Systems, 13(6) (2019) 771-782.
[23] Baali, Hamza, et al., Inequality indexes as sparsity measures applied to ventricular ectopic beats detection and its efficient hardware implementation, in IEEE Access 6 (2017) 9464-9472.
[24] P?awiak, Pawe?, and MoloudAbdar, Novel methodology for cardiac arrhythmias classification based on long-duration ECG signal fragments analysis, in Biomedical signal processing. Springer, Singapore, (2020) 225-272.
[25] Nahian Ibn Hasan, Arnab Bhattacharjee, Deep Learning Approach to Cardiovascular Disease Classification Employing Modified ECG Signal from Empirical Mode Decomposition, in Biomedical Signal Processing and Control 52 (2019) 128-140.
[26] M. Yin, R. T., Miao Liu, K. Han, X. Lv, M. Huang, P. Xu, Y. Hu, B. Ma, and Y. Gai, Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System, on Internet of Medical Things for Healthcare Engineering, (2021).
[27] Hassan Sharabaty, Ihab Ahkam, Abdellatif Baba, FPGA-Based Multi-Heart Diseases Classification System with the Aid of LabVIEW, in 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
[28] hubham Srivastava, Himanshu Bhardwaj, Aman Dixit, Prof. Namita Kalyan Shinde, ECG Pattern Analysis Using Artificial Neural Network, in IJETT International Journal of Electronics and Communication Engineering 7.5 (2020).
[29] Zhao, Yang, Zhongxia Shang, and Yong Lian, A 13.34 ?W event-driven patient-specific ANN cardiac arrhythmia classifier for wearable ECG sensors, in IEEE transactions on biomedical circuits and systems 14.2 (2019) 186-197.
[30] Moridani, M. K., M. AbdiZadeh, and Z. ShahiazarMazraeh, An efficient automated algorithm for distinguishing normal and abnormal ECG signal, IRBM 40.6 (2019) 332-340.
[31] Romdhane, TaissirFekih; Alhichri, Haikel; Ouni, Ridha; Atri, Mohamed Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss, in Computers in Biology and Medicine, (2020).